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Accurate and timely traffic flow prediction is crucial for intelligent transportation systems (ITS). Recent advances in graph-based neural networks have achieved promising prediction results. However, some challenges remain, especially regarding graph construction and the time complexity of models. In this paper, we propose a multi-stream feature fusion approach to extract and integrate rich features from traffic data and leverage a data-driven adjacent matrix instead of the distance-based matrix to construct graphs. We calculate the Spearman rank correlation coefficient between monitor stations to obtain the initial adjacent matrix and fine-tune it while training. As to the model, we construct a multi-stream feature fusion block (MFFB) module, which includes a three-channel network and the soft-attention mechanism. The three-channel networks are graph convolutional neural network (GCN), gated recurrent unit (GRU) and fully connected neural network (FNN), which are used to extract spatial, temporal and other features, respectively. The soft-attention mechanism is utilized to integrate the obtained features. The MFFB modules are stacked, and a fully connected layer and a convolutional layer are used to make predictions. We conduct experiments on two real-world traffic prediction tasks and verify that our proposed approach outperforms the state-of-the-art methods within an acceptable time complexity.

Objective:To establish optimal gestational weight gain (GWG) in Chinese pregnant women by Chinese-specific BMI categories and compare the new recommendations with the Institute of Medicine (IOM) 2009 guidelines.Design:Multicentre, prospective cohort study. Unconditional logistic regression analysis was used to evaluate the OR, 95 % CI and the predicted probabilities of adverse pregnancy outcomes. The optimal GWG range was defined as the range that did not exceed a 1 % increase from the lowest predicted probability in each pre-pregnancy BMI group.Setting:From nine cities in mainland China.Participants:A total of 3731 women with singleton pregnancy were recruited from April 2013 to December 2014.Results:The optimal GWG (ranges) by Chinese-specific BMI was 15·0 (12·8–17·1), 14·2 (12·1–16·4) and 12·6 (10·4–14·9) kg for underweight, normal weight and overweight pregnant women, respectively. Inappropriate GWG was associated with several adverse pregnancy outcomes. Compared with women gaining weight within our proposed recommendations, women with excessive GWG had higher risk for macrosomia, large for gestational age and caesarean section, whereas those with inadequate GWG had higher risk for low birth weight, small for gestational age and preterm delivery. The comparison between our proposed recommendations and IOM 2009 guidelines showed that our recommendations were comparable with the IOM 2009 guidelines and could well predict the risk of several adverse pregnancy outcomes.Conclusions:Inappropriate GWG was associated with higher risk of several adverse pregnancy outcomes. Optimal GWG recommendations proposed in the present study could be applied to Chinese pregnant women.

Air pollution introduces a major challenge for societies, where it leads to the premature deaths of millions of people each year globally. Massive deployment of air quality sensing devices and data analysis for the resultant data will pave the way for the development of real-time intelligent applications and services, e.g., minimization of exposure to poor air quality either on an individual or city scale. 5G and edge computing supports dense deployments of sensors at high resolution with ubiquitous connectivity, high bandwidth, high-speed gigabit connections, and ultralow latency analysis. This article conceptualizes AI-powered scalable air quality monitoring and presents two systems of calibrating low-cost air quality sensors and the image processing of pictures captured by hyperspectral cameras to better detect air quality. We develop and deploy different AI algorithms in these two systems on a 5G edge testbed and perform a detailed analytics regarding to 1) the performance of AI algorithms and 2) the required communication and computation resources.

Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. The heart is one of the most important oxygen delivery organs, and dysfunction significantly increases the mortality of the body. Hence, the heart has been studied in sepsis for over half a century. However, the definition of sepsis-induced cardiomyopathy is not unified yet, and the conventional conception seems outdated: left ventricular systolic dysfunction (LVSD) along with enlargement of the left ventricle, recovering in 7 to 10 days. With the application of echocardiography in intensive care units, not only LVSD but also left ventricular diastolic dysfunction, right ventricular dysfunction, and even diffuse ventricular dysfunction have been seen. The recognition of sepsis-induced cardiomyopathy is gradually becoming complete, although our understanding of it is not deep, which has made the diagnosis and treatment stagnate. In this review, we summarize the research on sepsis-induced cardiomyopathy. Women and young people with septic cardiomyopathy are more likely to have LVSD, which may have the same mechanism as stress cardiomyopathy. Elderly people with ischemic cardiomyopathy and hypertension tend to have left ventricular diastolic dysfunction. Patients with mechanical ventilation, acute respiratory distress syndrome or other complications of increased right ventricular afterload mostly have right ventricular dysfunction. Diffuse cardiac dysfunction has also been shown in some studies; patients with mixed or co-existing cardiac dysfunction are more common, theoretically. Thus, understanding the pathophysiology of sepsis-induced cardiomyopathy from the perspective of critical care echocardiography is essential.

The mechanism of business analytics affordances enhancing the management of cloud computing data security is a key antecedent in improving cloud computing security. Based on information value chain theory and IT affordances theory, a research model is built to investigate the underlying mechanism of business analytics affordances enhancing the management of cloud computing data security. The model includes business analytics affordances, decision-making affordances of cloud computing data security, decision-making rationality of cloud computing data security, and the management of cloud computing data security. Simultaneously, the model considers the role of data-driven culture and IT business process integration. It is empirically tested using data collected from 316 enterprises by Partial Least Squares-based structural equation model. Without data-driven culture and IT business process integration, the results suggest that there is a process from business analytics affordances to decision-making affordances of cloud computing data security, decision-making rationality of cloud computing data security, and to the management of cloud computing data security. Moreover, Data-driven culture and IT business process integration have a positive mediation effect on the relationship between business analytics affordances and decision-making affordances of cloud computing data security. The conclusions in this study provide useful references for the enterprise to strengthen the management of cloud computing data security using business analytics.

Context Our previous study found that Fengbaisan improved chronic obstructive pulmonary diseases (COPD). Objective To elucidate the mechanism of Fengbaisan in COPD. Materials and methods Rats in Model, FBS, FBS + DMSO and FBS + EX527 groups received cigarette smoke extract (CSE) inhalation and intratracheal instillation of lipopolysaccharide to establish COPD model. Normal group received room air and normal saline. The COPD rats were given Fengbaisan (1 mL/d) or combined with EX527 (5 mg/kg/2 d) by intraperitoneal injection. Human lung carcinoma (A549) cells were treated with 10% CSE, 10% serum-containing Fengbaisan or EX527. We observed lung percentage of forced expiratory volume in first 0.3 sec to forced vital capacity (FEV0.3/FVC), inspiratory resistance (RI) and lung dynamic compliance (Cdyn) of rats. The lung pathological changes, the number of inflammatory cells and neutrophils, inflammatory factor, apoptosis, gene and protein expression were examined. Results SIRT1 was downregulated in lung tissues of COPD rats and CSE-induced A549 cells. Fengbaisan enhanced FEV0.3/FVC (74.28%) and Cdyn (0.28 cm H2O/mL/s), and reduced RI (0.48 mL/cm H2O) of COPD rats. Moreover, Fengbaisan promoted SIRT1 expression, and repressed TIMP-1/MMP-9 expression. Fengbaisan enhanced apoptosis and the expression of GRP78, caspase-12 and caspase-3. The inflammatory factor levels, the number of inflammatory cells and neutrophils, and lung lesions were inhibited by Fengbaisan in COPD rats. The influence conferred by Fengbaisan was abolished by EX527. Discussion and conclusions Fengbaisan inhibits endoplasmic reticulum stress and inflammation reaction by up-regulating SIRT1 expression to improve COPD. Therefore, Fengbaisan may be an effective Chinese medicine for treating COPD.

Background: Increased right ventricle afterload during acute respiratory distress syndrome (ARDS) may induce acute cor pulmonale(ACP), which is associated with a poor clinical outcome. Echocardiography is now considered as a rapid and non-invasive tool for diagnosis of ACP. The aims of this study were to investigate the morbidity and mortality rates of ACP in ARDS patients in intensive care units (ICUs) across the mainland of China and to determine the severity and prognosis of ACP in ARDS patients through an ultrasound protocol (TRIP). And the association between ACP related factors and the ICU mortality will be revealed. Methods: This study is a multicenter and cross-sectional study in China which will include ICU participants when diagnosed as ARDS. The ultrasound protocol, known as the TRIP, is proposed as severity assessment for ACP, which includes tricuspid regurgitation velocity (T), right ventricular size (R), inferior vena cava diameter fluctuation (I), and pulmonary regurgitation velocity(P). The 28-day mortality, ICU/hospital mortality, the length of stay in ICU, mechanical ventilation days, hemodynamic parameters and lab parameters of liver function and kidney function are all recorded. Discussion: This large-scale study would give a sufficient epidemic investigation of ACP in ARDS patients in China. In addition, with the TRIP protocol, we expect that we could stratify ACP with more echocardiography parameters. Trial registration: NCT03827863, https://clinicaltrials.gov/ct2/show/NCT03827863 Key words: Acute respiratory distress syndrome; Acute cor pulmonale; Echocardiography; Outcome

Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis proximity to where data are captured based on artificial intelligence. Edge intelligence aims at enhancing data processing and protects the privacy and security of the data and users. Although recently emerged, spanning the period from 2011 to now, this field of research has shown explosive growth over the past five years. In this article, we present a thorough and comprehensive survey of the literature surrounding edge intelligence. We first identify four fundamental components of edge intelligence, i.e., edge caching, edge training, edge inference, and edge offloading based on theoretical and practical results pertaining to proposed and deployed systems. We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems, adopted techniques, and application goals. For each category, we elaborate, compare, and analyze the literature from the perspectives of adopted techniques, objectives, performance, advantages and drawbacks, and so on. This article provides a comprehensive survey of edge intelligence and its application areas. In addition, we summarize the development of the emerging research fields and the current state of the art and discuss the important open issues and possible theoretical and technical directions.

Acute respiratory distress syndrome (ARDS) is one of the most common severe diseases seen in the clinical setting. With the continuous exploration of ARDS in recent decades, the understanding of ARDS has improved. ARDS is not a simple lung disease but a clinical syndrome with various etiologies and pathophysiological changes. However, in the intensive care unit, ARDS often occurs a few days after primary lung injury or after a few days of treatment for other severe extrapulmonary diseases. Under such conditions, ARDS often progresses rapidly to severe ARDS and is difficult to treat. The occurrence and development of ARDS in these circumstances are thus not related to primary lung injury; the real cause of ARDS may be the “second hit” caused by inappropriate treatment. In view of the limited effective treatments for ARDS, the strategic focus has shifted to identifying potential or high-risk ARDS patients during the early stages of the disease and implementing treatment strategies aimed at reducing ARDS and related organ failure. Future research should focus on the prevention of ARDS.

Residents of Hong Kong have undergone a dietary transition from a traditional Chinese diet that is high in seafood to a more Western diet. This may have affected the nutritional composition of breast milk of Hong Kong mothers. The present study aims to investigate the relationship between the dietary pattern and the fatty acid profile of the breast milk of lactating women in Hong Kong. Seventy-three volunteering healthy Hong Kong lactating mothers participated in the study. Their dietary intakes were assessed by using a 3-d dietary record and FFQ. The meann-3 fatty acid levels were approximately 0·4 % (EPA) and 0·9 % (DHA) of total fatty acids in the breast milk of lactating mothers who had exclusively breastfed their infants aged 2–6 months. Maternal dietary intakes ofn-3 fatty acids were positively associated with their levels in the breast milk. The levels of maternal intakes of freshwater and saltwater fish, especially the consumption of salmon, croaker and mandarin, were significantly correlated with the content of DHA in breast milk. The present study is among the very few in the literature to determine the fatty acid profile of breast milk in Hong Kong populations and verify certain dietary factors that influence this profile. High levels ofn-3 PUFA, especially DHA, were observed in the breast milk of Hong Kong lactating women. The findings may serve as a dietary reference for lactating mothers to optimise the fatty acid profile of their breast milk.

Background: Acute kidney injury (AKI) is a serious complication in critically ill patients with septic shock treated in the intensive care unit. Renal replacement therapy (RRT) is a treatment for severe AKI; however, the time of initiation of RRT and factors that affect the recovery of kidney function remains unclear. This study was to explore whether early initiation of RRT treatment for fluid management to reduce central venous pressure (CVP) can help to improve patients’ kidney function recovery. Methods: A retrospective analysis of septic patients who had received RRT treatment was conducted. Patients received RRT either within 12 h after they met the diagnostic criteria of renal failure (early initiation) or after a delay of 48 h if renal recovery had not occurred (delayed initiation). Parameters such as patients’ renal function recovery at discharge, fluid balance, and levels of CVP were assessed. Results: A total of 141 patients were eligible for enrolment: 40.4% of the patients were in the early initiation group (57 of 141 patients), and 59.6% were in the delayed initiation group (84 of 141 patients). There were no significant differences in the characteristics at baseline between the two groups, and there were no differences in 28-day mortality between the two groups (χ2 = 2.142, P = 0.143); however, there was a significant difference in the recovery rate of renal function between the two groups at discharge (χ2 = 4.730, P < 0.001). More importantly, early initiation of RRT treatment and dehydration to reduce CVP are more conducive to the recovery of renal function in patients with AKI. Conclusion: Compared with those who received delayed initiation RRT, patients who received early-initiation RRT for dehydration to reduce CVP have enhanced kidney function recovery. Key words: Sepsis; Acute kidney injury; Renal replacement therapy; Central venous pressure

This paper reports the fabrication of subwavelength nano-structures on the curved surface of a glass lens for the purposes of anti-reflection. It utilizes a flexible PDMS mold to transfer metallic patterns to the lens surface which is pre-coated with a polymer layer. Using the transferred metal pattern as an etching mask, nano-structures are directly fabricated on the lens surface by plasma dry-etching processes. A plano/convex lens made of BK-7 glass with a lens diameter of 12.7 mm and a radius of curvature of 25.8 mm is used in this paper. Hexagonally arrayed nano-structures with a truncated conical profile, a center-to-center pitch of 300 nm, and a maximum structure height of 290 nm are experimentally achieved. Measurement results show the optical transmittance is increased by about 2.4% in average for wavelength within visible light spectrum. The directly fabricated nano-structures also exhibit good anti-glaring effects and the potentials for further optical applications.

Objective:. Sepsis is a deadly infection that causes injury to tissues and organs. Infection and anti-infective treatment are the eternal themes of sepsis. The successful control of infection is a key factor of resuscitation for sepsis and septic shock. This review examines evidence for the treatment of sepsis. This evidence is combined with clinical experiments to reveal the rules and a standard flowchart of anti-infection therapy for sepsis. Data Sources:. We retrieved information from the PubMed database up to October 2018 using various search terms and their combinations, including sepsis, septic shock, infection, antibiotics, and anti-infection. Study Selection:. We included data from peer-reviewed journals printed in English on the relationships between infections and antibiotics. Results:. By combining the literature review and clinical experience, we propose a 6Rs rule for sepsis and septic shock management: right patients, right time, right target, right antibiotics, right dose, and right source control. This rule encompasses rational decisions regarding the timing of treatment, the identification of the correct pathogen, the selection of appropriate antibiotics, the formulation of a scientifically based antibiotic dosage regimen, and the adequate control of infectious foci. Conclusions:. This review highlights how to recognize and treat sepsis and septic shock and provides rules and a standard flowchart for anti-infection therapy for sepsis and septic shock for use in the clinical setting.

Physical world enhancements through virtually drawn annotations on mobile devices are a core component of mobile augmented reality (MAR) experiences. However, resource intensive computer vision tasks typically limit the full deployment of MAR pipelines on-device due to a lack of required computation resources. As an alternative, a distributed scheme between client devices and edge servers enables offloading computation intensive tasks to powerful machines. This introduces challenges, such as maintaining low communication latency and high bandwidth channels between client and edge server. Current LTE and WiFi infrastructure do not fulfil such requirements. 5G networks are an ideal candidate to provide seamless MAR experiences. This research investigates the enhancement of MAR with both 5G and edge computing and aims to address two particular challenges: 1) the creation of a new 5G-based edge computing infrastructure to support reliable MAR, and 2) investigating which particular transport protocols allow for optimised quality of experience (QoE) for MAR applications within 5G infrastructure.

With the rapid development of the P2P (peer-to-peer) online lending industry, which is facing significant credit risk, personal credit evaluation is an important method to reduce credit risk. Based on the various indexes of personal credit risk evaluation of domestic and foreign commercial banks, and according to the characteristics of P2P online lending, this paper analyzes the factors that affect the credit risk of P2P online borrowers, introduces the unique risk factors in the field of Internet information, and constructs an index system of personal credit risk evaluation of P2P online lending, which combines qualitative and quantitative indexes, including six major indexes and 21 small indexes. It then quantifies each index and defines the judgment standard of the evaluation results. Using analytic hierarchy process (AHP), expert scoring method, and fuzzy comprehensive evaluation method, this paper establishes a personal credit risk evaluation model of P2P online lending based on AHP method. The public information of two borrowers on the &ldquo, PaiPai Lending&rdquo, platform are selected for experimental verification. The results show that the improved personal credit risk evaluation model has better applicability and can evaluate the borrower&rsquo, s credit status more scientifically, accurately, and comprehensively, thus, it is an effective method of personal credit risk evaluation of P2P online lending.

This work has been partially supported by the UK EPSRC under grant number EP/N007565/1, “Science of Sensor Systems Software”, and by Academy of Finland projects, grant number 325774, 3196669, 319670, 326305, and 325570. The advances of sensing and computing technologies pave the way to develop novel applications and services for wearable devices. For example, wearable devices measure heart rate, which accurately reflects the intensity of physical exercise. Therefore, heart rate prediction from wearable devices benefits users with optimization of the training process. Conventionally, Cloud collects user data from wearable devices and conducts inference. However, this paradigm introduces significant privacy concerns. Federated learning is an emerging paradigm that enhances user privacy by remaining the majority of personal data on users’ devices. In this paper, we propose a statistically sound, Bayesian inference federated learning for heart rate prediction with autoregression with exogenous variable (ARX) model. The proposed privacy-preserving method achieves accurate and robust heart rate prediction. To validate our method, we conduct extensive experiments with real-world outdoor running exercise data collected from wearable devices. Postprint

Alkaloids are a class of secondary metabolites that can be derived from plants, fungi and marine sponges. They are widely known as a continuous source of medicine for the management of chronic disease including cancer, diabetes and neurodegenerative diseases. For example, galanthamine and huperzine A are alkaloid derivatives currently being used for the symptomatic management of neurodegenerative disease. The etiology of neurodegenerative diseases is polygenic and multifactorial including but not limited to inflammation, oxidative stress and protein aggregation. Therefore, natural-product-based alkaloids with polypharmacology modulation properties are potentially useful for further drug development or, to a lesser extent, as nutraceuticals to manage neurodegeneration. This review aims to discuss and summarise recent developments in relation to naturally derived alkaloids for neurodegenerative diseases.

Plates of aluminum have the advantages of low density, high strength and corrosion resistance, and are widely used in aviation, aerospace, shipbuilding and other industrial fields. Performing defect detection and structural health monitoring is of great significance for ensuring the health of facilities and maintaining normal production and use. Lamb waves can propagate long distances with low attenuation in metal plates to achieve long-distance non-destructive testing. Laser detection technology has been applied in the field of non-destructive testing and has the advantages of high spatial resolution and non-contact. In this study, Full laser-based Lamb waves array imaging based on the two-dimensional multiple signal classification algorithm is proposed to locate and image defects in the aluminum plate. A full laser ultrasonic detection system is used to excite and receive laser-based Lamb wave signals. The array is cross-shaped and is used to collect the data. Laser-based Lamb wave signals with wideband and low time domain resolution are processed by continuous wavelet transform to obtain a narrowband signal with certain central frequency. Finally, the two-dimensional multiple signal classification algorithm is used to realize the localization and imaging of defects in the aluminum plate.

Wearable devices collect user information about their activities and provide insights to improve their daily lifestyles. Smart health applications have achieved great success by training Machine Learning (ML) models on a large quantity of user data from wearables. However, user privacy and scalability are becoming critical challenges for training ML models in a centralized way. Federated learning (FL) is a novel ML paradigm with the goal of training high quality models while distributing training data over a large number of devices. In this demo, we present FL4W, a FL system with wearable devices enabling training a human activity recognition classifier. We also perform preliminary analytics to investigate the model performance with increasing computation of clients.

BACKGROUND: Connexins (Cxs) are reported to participate in atherosclerosis associated intimal hyperplasia (IH), while their function involved in the balloon injury (BI) induced IH and restenosis is less reported. METHODS: Forty-eight male Sprague-Dawley rats were randomly assigned to not injured (NI) group and BI group, which were further administrated with ERK-inhibitor U0216 and Akt-inhibitor MIK2206. Western blot and RT-PCR were utilized to detect the expression of Cx30, Cx37, Cx40, and Cx43 at 6 hours, 24 hours, 7 days, and 14 days post-surgery. H&E staining and related intima area, media area, and luminal area measurement were applied to indicate neointima formation and IH. ERK and Akt phosphorylation levels and proliferating cell nuclear antigen (PCNA) immunostaining were also detected. RESULTS: Among the four Cxs detected, Cx37 showed down-regulated, and Cx43 showed up-regulated temporal expression pattern in BI rats with confirmed neointima formation. Up-regulated p-ERK (P

Mobile Augmented Reality (MAR) provides a unique experience where the physical world is augmented with virtual annotations. MAR involves computation-heavy algorithms that could potentially be offloaded to edge servers on 5G networks, which significantly enhances MAR with reduced communication latency and more stable network connections, therefore leading to seamless MAR user experiences. In this demo, we show a running MAR system deployed on a 5G edge test bed and present latency results.

Objectives: Plant oil for cooking typically provides 40% to 50% of dietary fat, 65% of linoleic acid, 44% of α-linolenic acid and 41% of oleic acid in the Chinese diet. However, the comparative effects of fatty acids derived from plant oil on cardiovascular risk factors in Chinese are still inconclusive. Hence, the aim of this study is to investigate whether cardiovascular risk factors are altered depending on various types of plant oils such as peanut oil rich in oleic acid, corn oil rich in linoleic acid, and blend oil fortified by α-linolenic acid. Design: A randomized, double-blinded, parallel-designed trial. Setting: The First and the Second Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. Participants: A total of 251 volunteers with fasting blood total cholesterol between 5.13 and 8.00 mmol L−1 were enrolled. Intervention: Volunteers received peanut oil, corn oil or blend oil to use for cooking for one year. Main outcome measures: The erythrocyte membrane fatty acid composition, fasting plasma lipids, glucose and insulin concentrations and high sensitivity C-reactive protein (hsCRP) levels were measured before, during and after the intervention. The level of α-linolenic acid in erythrocyte membranes was significantly increased in the blend oil group after the intervention (P < 0.001). The level of other fatty acids did not show any statistically significant differences between the three groups. No significant differences were observed in the concentrations of fasting plasma lipids, hsCRP, glucose, and insulin among the three groups using different types of plant oils. Conclusions: The results suggest that although ingesting cooking oil with different fatty acid composition for one year could change erythrocyte membrane fatty acid compositions, it did not significantly modify cardiovascular risk factors in moderately hypercholesteremic people.

Summary Background & aims Previous studies linking the gut microbiome with childhood obesity largely used the body mass index to measure obesity and reached inconsistent findings. Little evidence has linked the gut microbiome to regional body fat deposition. We investigated whether the abundance of specific taxa in the gut microbiota and the concentrations of short-chain fatty acids (SCFAs) were associated with the content and regional deposition of body fat in children. Methods This cross-sectional study involved 236 children aged 6–9 years. The fat mass contents/percentages in the total body and the android, gynoid, and limb regions were determined by dual-energy X-ray absorptiometry, and the android-to-gynoid fat mass ratio and fat-to-lean mass ratios were calculated. Fecal samples were subjected to16S rRNA amplicon sequencing, and the fecal SCFA concentrations were quantified via high-performance liquid chromatography. Results A weighted gene co-expression network analysis identified seven modules of co-expressed operational taxonomic units (OTUs). A total of 57 OTUs from 4 key modules were selected for further analysis. After adjustment of covariates and controlling for the false discovery rate (FDR), a multiple linear regression analysis revealed significant correlations of the abundances of some OTUs with obesity and body fat measures. For instance, the OTUs classified to the species Ruminococcus gnavus and Flavonifractor plautii showed significant negative correlations with the total and regional body fat (β: −0.250 to −0.180, PFDR: 0.041–0.049), whereas OTUs belonging to the genera Blautia and Romboutsia exhibited positive correlations with body fat measures (β: 0.184–0.222, PFDR: 0.041–0.049). The fecal concentrations of acetic, propionic, and butyric acids and total SCFAs were significantly positively correlated with various parameters of body fat distribution (β: 0.160–0.275, PFDR: Conclusion The gut microbiome and SCFAs are significantly associated with obesity and body fat distribution in pediatric population.

The rapid increase of Internet of Things (IoT) applications and services has led to massive amounts of heterogeneous data. Hence, we need to re-think how IoT data influences the network. In this paper, we study the characteristics of IoT data traffic in the context of smart cities. Aiming at analyzing the influence of IoT data traffic on the access and core network, we generate various IoT data traffic according to the characteristics of different IoT applications. Based on the analysis of the inherent features of the aggregated IoT data traffic, we propose a Long Short-Term Memory (LSTM) model combined with autoregressive spectrum analysis to predict the IoT data traffic. In this model, the autoregressive spectrum analysis is used to estimate the minimum length of the historical data needed for predicting the traffic in the future, which alleviates LSTM's performance deterioration with the increase of sequence length. A sliding window enables predicting the long-term tendency of IoT data traffic while keeping the inherent features of the data traffic. The evaluation results show that the proposed model converges quickly and can predict the variations of IoT traffic more accurately than other methods and the general LSTM model.

Advances in technology equip traffic domain with instruments to gather and analyse data for safe and fuel-efficient traveling. In this article, we elaborate on the effects that taxi drivers' route selection has on fuel efficiency. For this purpose, we fuse real driving behaviour data from taxi cabs, weather, digital map, and traffic situation information to gain understanding of how the routes are selected and what are the effects in terms of fuel-efficiency. Analysis of actually driven trips and their quickest and shortest counterparts is conducted to find out the fuel-efficiency consequences on route selection. The judgments are used for developing a fuel-consumption model, exploring further the route characteristics and external factors affecting fuel consumption.

Fund investment is a hot issue in today’s society. How to choose a project for investment is affected by many factors. In view of this problem, this paper starts from the granular computing point of view and combines the multigranulation rough set decision-making method to construct a fund investment decision information system; then, the fund investment decision information system is reduced under different thresholds, and the decision rules are extracted through reduction. And from the aspects of decision accuracy and rule accuracy, the rules are analyzed. Finally, decision rules are used to give the decision of the fund investment project. This study provides a new approach to fund management.

Learning pollen transport mechanisms is the basis for correct interpretation of fossil pollen data. Firstly, 33 seabed surface sediment samples from the northeastern Indian Ocean were selected for pollen analysis. Utilizing the 90°E Indian Ridge as a dividing line, more Trilete spores (mainly Microlepia)and herbaceous pollen were present to the west or northwest of the line, possibly originating in Sri Lanka and India. More Polypodiaceae spores and tree pollen (Pinus excluded) were present to the east or southeast of the boundary, possibly originating in Sumatra Island. Based on the PCA analysis, the ocean current from the southeast to the northwest and the distance from land masses around the study area affect the percentage of pollen. Then, based on a fossil pollen analysis on the YDY05 core from the northeastern Indian Ocean, we determined that since 16 ka BP, the pollen terrestrial area might change from Sumatra Island to Sri Lanka Island and India, probably due to the climate and sea level change.

Background This study was designed to examine the effect of transmembrane protease serine 2 ETS-related gene (TMPRSS2-ERG) fusion on regulatory T cells and tumor growth in prostate cancer, which may provide a new potential therapeutic direction for PCa. Methods The effect of TMPRSS2-ERG fusion on the migration of Treg cells and tumor growth in a mouse model was investigated in vitro and in vivo. TMPRSS2-ERG fusion in biopsy tissues was performed by fluorescence in situ hybridization and the expression of ERG and Forkhead box P3 was detected by gel electrophoresis, real-time quantitative reverse transcription polymerase chain reaction and Western blot. Enzyme-linked immunosorbent assay and flow cytometry were used to analyze transforming growth factor β levels and the number of regulatory T cells, respectively. Finally, the infiltration of regulatory T cells was analyzed by Forkhead box P3 immunohistochemistry. Results Fluorescence in situ hybridization analysis showed that the TMPRSS2-ERG fusion gene was positive in prostate cancer and that the messenger RNA and protein expression of ERG were significantly up-regulated in prostate cancer biopsy tissues. Furthermore, the number of regulatory T cells and the levels of Forkhead box P3 and transforming growth factor β were significantly increased in prostate cancer. TMPRSS2-ERG fusion increased the migration and activation of regulatory T cells in vitro and promoted subcutaneous tumor size and regulatory T cells infiltration in mouse models. Conclusions TMPRSS2-ERG fusion can regulate the recruitment and infiltration of regulatory T cells to promote tumor growth in prostate cancer.

This cross-over experimental study aimed to examine the effects of filled chocolate consumption on blood glucose, insulin and ghrelin levels in 20 volunteers. After a one-week run-in period, study participants consumed two chocolate-based products, the tested biscuit or water for 21 days as a morning snack. After a two-week wash-out period, participants consumed another tested food for another 21 days. Each participant consumed all four test foods within an 18-week period. The participants' blood insulin increased slowly after two chocolate-based products intakes on the first day and satiety levels after eating chocolate-based products and the tested biscuit were the same. Chocolate consumption for three weeks had no adverse effects on blood glucose, insulin or ghrelin levels. In conclusion, compared to eating the tested biscuit, 21-day consumption of the tested chocolate-based products had no adverse effects on the blood glucose, insulin and ghrelin levels. This trial is registered with chictr.org.cn: ChiCTR-IOR-16009525.

This study empirically examines the effect of green finance and fintech on high-quality economic development. Using data from 30 provinces and municipalities in China from 2007 to 2019, this study applies panel regression analysis to investigate the relationships between green finance, fintech, and high-quality economic development, and adopts a two-step generalized method of moments (GMM) to defaecate the endogeneity issue. This study finds that green finance comprehensively facilitates high-quality economic development by positively affect its all three aspects (i.e., ecological environment, economic efficiency, and economic structure). Additionally, fintech strengthens the positive impact of green finance in the aspects of ecological environment and economic structure, while having limited effects on the relationship between green finance and economic efficiency. Based on these findings, our study proposes three policy suggestions for policymakers, including reinforcing the integration of fintech development with green finance, building an environmental disclosure framework to supervise local governments in improving efficiency of green finance, and developing medium- and long-term favorable policies as an external intervention measure to promote green finance in the private sector.

Hosting the deep learning model on the cloud may not be the best solution in many cases, for instance, IoT applications or autonomous system where low latency or enhanced security is desirable. Deep learning on the edge alleviates the above issues, and provides benefits of local computation. In this paper, we present the development of an open ISA (instruction set architecture) general purpose GPU aimed at edge computation. Our GPU, CASLab GPU, uses license-free, royalty-free HSAIL ISA specification and supports OpenCL1.2/2.0 APIs for heterogeneous computing. CASLab GPU also supports TensorFlow framework with CUDA-on-CL technology. CASLab GPU IP with configurable SIMT Core design tailors directly to the computing need of on-device learning and inference. The GPU is developed in ESL design methodology which incorporates GPU micro-architecture exploration, power modelling of the GPU, and the co-simulation of the GPU software stack.

The transformation of big data has quietly infiltrated people's lives. Some advanced companies in the petroleum industry have tried to use big data analysis to more accurately determine well locations, reduce drilling accident rates, and reduce exploration, gathering, refining, and sales. Cost. In short, big data has become the future development trend of the petroleum industry. But, there are still many problems in the application of big data in oilfield companies. In this paper, a framework of big data fusion and sharing exchange platform for oilfield enterprises is provided. The platform can improve the efficiency of enterprise personnel, enhance their comprehensive capabilities, and strengthen their core competitiveness.

Background. Increased extravascular lung water (EVLW) in shock is common in the critically ill patients. This study aimed to explore the effect of cardiac output (CO) on EVLW and its relevant influence on prognosis. Methods. The hemodynamic data of 428 patients with pulse-indicated continuous CO catheterization from Department of Critical Care Medicine, Peking Union Medical College Hospital were retrospectively collected and analyzed. The patients were assigned to acute respiratory distress syndrome group, cardiogenic shock group, septic shock group, and combined shock (cardiogenic and septic) group according to their symptoms. Information on 28-day mortality and renal function was also collected. Results. The CO and EVLW index (EVLWI) in the cardiogenic and combined shock groups were lower than those in the other groups (acute respiratory distress syndrome group vs. cardiogenic shock group vs. septic shock group vs. combined shock group: CO, 5.1 [4.0, 6.2] vs. 4.7 [4.0, 5.7] vs. 5.5 [4.3, 6.7] vs. 4.6 [3.5, 5.7] at 0 to 24 h, P = 0.009; 4.6 [3.8, 5.6] vs. 4.8 [4.1, 5.7] vs. 5.3 [4.4, 6.5] vs. 4.5 [3.8, 5.3] at 24 to 48 h, P = 0.048; 4.5 [4.1, 5.4] vs. 4.8 [3.8, 5.5] vs. 5.3 [4.0, 6.4] vs. 4.0 [3.2, 5.4] at 48 to 72 h, P = 0.006; EVLWI, 11.4 [8.7, 19.1] vs. 7.9 [6.6, 10.0] vs. 8.8 [7.4, 11.0] vs. 8.2 [6.7, 11.3] at 0 to 24 h, P 0.05). In addition, there was no significant difference in tissue perfusion or renal function among the four groups during the observation period (P > 0.05). However, the cardiogenic shock group had a higher 28-day survival rate than the other three groups [log rank (Mantel-Cox) = 31.169, P

With the growth of the automotive electronics technology, the advanced driver assistance system (ADAS) becomes more and more important. Especially, the moving object detection (MOD) is an important issue in the ADAS in intelligent vehicles. In realistic systems, there exist two critical challenges including computing time and detection rate for MOD. To overcome these problems, we propose a novel moving object detection system which contains pre-processing, feature extraction, classification and state machine. The pre-processing contains ROI extraction and skipping low busyness windows, which accelerates the computing time to solve the mentioned problem. To improve the performances, in this paper, the weighted histogram of oriented uniform gradient (WHOUG) with support vector machine (SVM) is proposed to promote the detection accuracy. Besides, the finite state machine could further improve the robustness of the proposed system. The results demonstrate that the proposed system achieves better performance than the traditional one, and also maintains real time computation.

Augmented reality head-worn computers often feature small-sized touch interfaces that complicate interaction with content, provide insufficient space for comfortable text input, and can be awkward to use in social situations. This paper presents a novel one-handed thumb-to-finger text entry solution for augmented reality head-worn computers. We design a glove composed of 12 force-sensitive nodes featuring an ambiguous keyboard layout. We first explore the viability of force disambiguation to evaluate the force division within the force spectrum. We select a 3-level force division as it allows to considerably reduce the number of keys while featuring a high (83.9%) accuracy. Following this pilot study, we map the 26 English characters onto the 9 nodes located on the index, middle and ring fingers in a 3-3-3 configuration, and attribute the space, enter and backspace keys to the remaining three nodes. We consider text entry performance as a quadmetric optimization problem considering the following criteria: goodness of character pairs, layout similarity to the QWERTY keyboard, easiness of force interaction, and comfort level of thumb reach. The resulting layout strikes a balance between performance and usability. We finally evaluate the quadmetric optimized layout over 6 sessions with 12 participants. The participants achieve an average text entry rate of 6.47 WPM with 6.85% error rate in the final session, which is significantly faster than existing thumb-to-finger solutions. In addition, our one-handed text entry system enhances the user mobility compared to other state-of-the-art solutions by freeing one hand, while allowing the user to direct his visual attention to other activities.

This report introduced and summarized the nursing care experience for a senior patient with lung cancer and developed programmed cell death protein 1 (PD-1) immunotherapy-related myocarditis combined with coronary heart disease (CHD) after receiving said treatment. In this case, immune myocarditis with CHD occurred shortly after implementing the PD-1 immunotherapy, yet the patient presented no clinical symptoms. Frequent nursing attention and close observation are so required for monitoring the patient's status and updating the physicians for a swift control of the myocarditis. For this case, nursing care procedures vital for the successful recovery of the patient included condition observation, position management, pre- and postcoronary angiography care, infection prevention, hemorrhage prevention, venous access port maintenance, pain care, trachea care, psychological care, diet care, environment management, and health education. After receiving effective, successful treatment and care, the patient was discharged after 13 days of treatment with generally satisfying overall conditions.

Cutting force, temperature, and stress on a cutting tool reflect the working condition of the cutting tool. Among these, the cutting force is relatively easier to be monitored and more reflective of the machining process stability. In this study, a new model of the milling force applied for a complex profile milling cutter has been developed based on the infinitesimal method. A dynamic contact model has been implemented through analyzing the working status of each section on the complex milling cutter. An underlying database is established in the form of response surfaces from simulation. The model of milling force for a complex profile milling cutter has been validated to be accurate by the comparison between the calculated and measured milling forces.

This cross-sectional study has been performed to investigate the relationship between serum carotenoids and bone mineral density (BMD) in Chinese population. We found that women with higher serum β-cryptoxanthin, lycopene, or α-carotene exhibited higher BMD at various bone sites. Similar association was observed between α-carotene and BMD in men. Carotenoids may positively regulate bone metabolism through their antioxidant properties; however, few studies have examined the relation between serum carotenoids and bone health. We aimed to determine the associations between the serum concentration of several carotenoid subclasses and BMD in a Chinese population. This study was a community-based cross-sectional study. We measured 1898 women and 933 men aged 59.6 years who completed serum β-cryptoxanthin, zeaxanthin + lutein, lycopene, and α-carotene concentration analyses and BMD assessments. Serum individual carotenoids were assessed by the methods of reverse-phase high-performance liquid chromatography. Dual-energy X-ray absorptiometry was applied to determine BMD at whole body, lumbar spine, total hip, femur neck, and trochanter. ANCOVA was used to examine the correlations between categorized individual carotenoids and BMD at measured sites. After adjusting for potential covariates, a monotonic dose–response positive correlation between circulating levels of β-cryptoxanthin, lycopene, and α-carotene and BMD at various skeletal sites was observed in women. Women in the top (vs. bottom) quartiles of serum β-cryptoxanthin, lycopene, or α-carotene exhibited 1.8–2.3, 1.5–2.0, or 1.3–2.7 % higher BMD at the bone sites with significant results (P-trend

Health applications involve many data sources, individuals, and services that work against guarantees that an individual's personal data will not be used without consent. The proposed privacy-centered architecture integrates data security and semantic descriptions into a trust-query framework, enabling the provision of user consent as a service.

Long-chain polyunsaturated fatty acids (LC-PUFA), especially linoleic acid (LA), arachidonic acid (AA), alphalinolenic acid (ALA), eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), are essential to infant growth and development during early life. Up till now, there is only limited number of studies with large sample size on LC-PUFA in breast milk in China. In order to better understand the LC-PUFA levels in Chinese women, we report an analysis that included 44 studies of 3815 subjects from the latest 10-year peer-reviewed papers of breast milk studies of fatty acid profiles from China and other regions: Asia (excluding China), Europe, America and Africa. The results showed that Chinese women had significantly higher LA and ALA levels in the breast milk than European and other Asian women. The DHA level in the breast milk of Chinese women meets the Chinese and international recommended intake for young infants. This review provides comprehensive investigation on the LC-PUFA levels in the mature milk of Chinese women in most recent publications and server as a reference for further studies on human milk in China.

There exist some obstacles to realize high-precision positioning with using the position differential technique due to the limiting factors. However, with the advancement of chip performance and the establishment of multiple satellite navigation systems, position differential positioning can be realized to meet the civilian high-precision positioning demand while significantly reducing costs. This paper is to explore the engineering application and propose a new method. Through the experiments and data analysis, we have found the error correlation between two receivers in a small area and designed a set of positioning system, and programmed in the Keil µ Vision5 in C language. The system consists of the current mainstream low-cost STM32, UBLOX NEO 6M chip, NRF24L01 chip, GPS antenna and power supply module. Each two receivers can form a differential positioning system. At present, the system in the low-speed status is, in the urban environment, basically reaching within 0.5 – 3 m of the positioning error.

SCOPE: To compare the effects of supplementary eicosapentaenoic acid (EPA)+docosahexaenoic acid (DHA) versus α‐linolenic acid (ALA) on lipid profiles, inflammatory status, and fatty acid composition of peripheral blood mononuclear cells (PBMCs) in hypercholesterolemic adults. METHODS AND RESULTS: A randomized, controlled, double‐blind trial is conducted to examine the effects of consumption of control oil, 4.2 g/d ALA, 7.2 g/d ALA, 1.8 g/d DHA+EPA, or 3.6 g/d EPA+DHA for 12 weeks on lipid profiles, fatty acid composition of PBMCs and in vitro production of interleukin‐6 (IL‐6) and tumor necrosis factor‐α (TNF‐α) by PBMCs in 123 subjects with hypercholesteremia. After the intervention, subjects who receive a low and high dose of DHA/EPA experience 11.99% and 15.78% decreases in triglycerides which is significantly different from that of the control group (p < 0.05). The in vitro study indicates that supplementation of high‐dose DHA+EPA induces the greatest decrease of IL‐6 production by PBMCs relative to other groups (p = 0.046). ALA intervention significantly increases the PBMCs composition of ALA but not EPA/DHA. CONCLUSION: EPA+DHA, but not ALA, improves lipids and inflammation status in hypercholesterolemic adults. Supplementation of ALA does not increase the PBMCs composition of EPA/DHA in middle‐aged to elderly Chinese.

Osteoporosis is one of the major attributing factors to the deterioration of quality of life and disability among geriatric populations. The low population mean calcium intakes due to the traditional dietary pattern in China and Hong Kong elevate the risk of osteoporosis. The critical window of dietary intervention to prevent osteoporosis would be the premenarcheal period for the female. Cross-sectional studies in China showed consistent results with studies with different ethnicities that dietary calcium intake and weight-bearing physical activity level were significantly correlated with bone mineral content (BMC) and bone mineral density (BMD). Studies also indicated that premenarche Chinese girls achieved maximum calcium retention and maximum bone accretion at 1100 mg and 1200 mg per day, respectively. In the Chinese population, polymorphisms in several genes including interleukin-6 (IL-6) genes, estrogen receptor genes, parathyroid hormone (PTH) genes, osteoprotegerin (OPG) gene, calcium-sensing receptor (CASR) genes, and vitamin D receptor (VDR) genes were identified to interact with different environmental factors in a specific manner contributing to the bone health.

Computational offloading can improve user experience of mobile apps through improved responsiveness and reduced energy footprint. A fundamental challenge in offloading is to distinguish situations where offloading is beneficial from those where it is counterproductive. Currently, offloading decisions are predominantly based on profiling performed on individual devices . While significant gains have been shown in benchmarks, these gains rarely translate to real-world use due to the complexity of contexts and parameters that affect offloading. We contribute by proposing crowdsensed evidence traces as a novel mechanism for improving the performance of offloading systems. Instead of limiting to profiling individual devices, crowdsensing enables characterizing execution contexts across a community of users, providing better generalisation and coverage of contexts. We demonstrate the feasibility of using crowdsensing to characterize offloading contexts through an analysis of two crowdsensing datasets. Motivated by our results, we present the design and development of the EMCO toolkit and platform as a novel solution for computational offloading. Experiments carried out on a testbed deployment in Amazon EC2 Ireland demonstrate that EMCO can consistently accelerate app execution while at the same time reduce energy footprint. We also demonstrate that EMCO provides better scalability than current cloud platforms, being able to serve a larger number of clients without variations in performance. Our framework, use cases, and tools are available as open source from GitHub.

IoT services hosted by low-power devices rely on the cloud infrastructure to propagate their ubiquitous presence over the Internet. A critical challenge for IoT systems is to ensure continuous provisioning of IoT services by overcoming network breakdowns, hardware failures, and energy constraints. To overcome these issues, we propose a cloud-based framework namely SensorClone, which relies on virtual devices to improve IoT resilience. A virtual device is the digital counterpart of a physical device that has learned to emulate its operations from sample data collected from the physical one. SensorClone exploits the collected data of low-power devices to create virtual devices in the cloud. SensorClone then can opportunistically migrate virtual devices from the cloud into other devices, potentially underutilized, with higher capabilities and closer to the edge of the network, e.g., smart devices. Through a real deployment of our SensorClone in the wild, we identify that virtual devices can be used for two purposes, 1) to reduce the energy consumption of physical devices by duty cycling their service provisioning between the physical device and the virtual representation hosted in the cloud, and 2) to scale IoT services at the edge of the network by harnessing temporal periods of underutilization of smart devices. To evaluate our framework, we present a use case of a virtual sensor created from an IoT service of temperature. From our results, we verify that it is possible to achieve unlimited availability up to 90% and substantial power efficiency under acceptable levels of quality of service. Our work makes contributions towards improving IoT scalability and resilience by using virtual devices.